How a fashion brand improved discovery in conversational search

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Miss Chase sells affordable Western wear to young consumers across India. With over 2,000 products and a strong marketplace presence, the brand operates in one of retail's most competitive categories. When consumer search behavior started shifting toward conversational interfaces, the team wanted to understand how their products would show up in this new environment.
BACKGROUND
Consumer discovery is evolving.
With over 2,000 products across multiple categories, maintaining detailed, structured content at scale proved challenging. The team wanted to enhance product information with richer fabric details, fit guidance, and styling context, but doing this manually across the entire catalog wasn't operationally feasible. The question: Could structured product optimization improve visibility in AI answerswhen customers ask about women's fashion and apparel categories?
HOW IT'S DONE

Evaluate AI discovery readiness

APProach
Results
What Changed
Observations

Miss Chase worked with Glu to test content optimization across 12 strategic products.

These included hero items, fast movers, and newly launched collections. For each product, Glu generated structured descriptions with:

• Intent-aligned product titles
• Structured 150-250 word descriptions
• Clear fabric and material details
• Fit and sizing explanations
• Styling and occasion guidance
• 4–6 structured FAQs
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The client reviewed the optimized content and provided detailed feedback on fabricspecificity, fit clarity, and styling relevance. This feedback was incorporated into iterative refinements before the content was published to Shopify. Weekly tracking measured how often Miss Chase products appeared in responses to relevant queries.
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Within 10 weeks, visibility in tracked queries improved from 0% to 18%.

In a crowded apparel market dominated by brands like Zara, H&M, and Levi's, achieving this level of presence demonstrated that structured product information directly impacts inclusion in conversational search results.
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The improvements weren't about keyword density. They came from clarity and completeness. Before optimization, product pages had minimal attribute detail and no structured FAQs.After optimization, each page included explicit fabric composition, clear fit signals, occasion mapping, and answers to common questions.
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The pilot showed that visibility in conversational search depends heavily on how well product information is organized and articulated. For apparel brands managing thousands of SKUs, this creates both a challenge and an opportunity.
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During the pilot period, measured indicators showed directional changes in how Chase Haul appeared within AI-generated answers.

Some variation was observed from week to week, as expected in early-stage testing. Overall, the results indicated improved consistency and accuracy in how Chase Haul products were surfaced in relevant AI contexts.
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The optimized product descriptions and FAQs are great. We could make these live with minimal edits. Further, the visibility improvements of our products week after week are giving us confidence in how GEO is benefiting our brand in a crowded market.
Sreeja Ray, Marketing & Business Operations Head, Chase Haul

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